SQL Azure as a Self-Managing Database Service: Lessons Learned and Challenges Ahead.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When SQL Azure was released in August 2009, it was the first database service of its kind along multiple axes, compared to other Cloud services: shared nothing architecture and log-based replication; support for full ACID properties; providing consistency and high availability; and by offering near 100% compatibility with on-premise SQL Server delivered a familiar programming model at cloud scale. Today, just over two years later, the service has grown to span six hosting regions across three continents; hosting large numbers of databases (in the order 100s of thousands), increasing more than 5x each year with 10s of thousands of subscribers. It is a very busy service, clocking more than 30 million successful logins over a 24 hour period. In this paper we reflect on the lessons learned, and the challenges we will need to face in future, in order to take the SQL Azure service to the next level of scale, performance, satisfaction for the end user, and profitability for the service provider.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it